N8N QDrant workflow expert

n8n + Qdrant: Ingestion & RAG Pipeline Skill

Overview

This skill enables AI agents to design, build, and troubleshoot production-grade Qdrant ingestion and retrieval pipelines in n8n. It covers the full lifecycle: source data extraction → chunking → metadata enrichment → vector embedding → Qdrant upsert → retrieval (dense, sparse, hybrid) → RAG response generation.

Always read the supporting docs in /docs/ before building workflows:

docs/NODE-REFERENCE.md — Every Qdrant node, mode, and parameter explained

docs/INGESTION-PIPELINE.md — Step-by-step ingestion architecture

docs/RAG-RETRIEVAL.md — Dense, sparse, and hybrid retrieval patterns

docs/CHUNKING-METADATA.md — Chunking strategies and metadata schema design

docs/examples/ — Annotated workflow JSON examples

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Two Node Systems to Know

n8n has two separate Qdrant integration systems — knowing which to use is critical:

1. Official Qdrant Node (n8n-nodes-qdrant)

Package: n8n-nodes-qdrant (community node, install via n8n Settings → Community Nodes)

Purpose: Direct Qdrant API operations — collection management, point upsert/delete/scroll, search queries

Node name in editor: Qdrant

Use for: Building custom ingestion pipelines, running Query Points (dense/sparse/hybrid search), collection setup, point management

GitHub: https://github.com/qdrant/n8n-nodes-qdrant

2. LangChain Vector Store Node (built-in)

Package: Built into n8n's AI/LangChain nodes

Purpose: LangChain-compatible vector store integration — connects with Document Loaders, Text Splitters, Embeddings, and AI Agents

Node name in editor: Qdrant Vector Store (@n8n/n8n-nodes-langchain.vectorStoreQdrant)

Use for: LangChain-style RAG pipelines, AI Agent tool integration, retrieve-as-tool mode

Modes: insert (ingest documents), retrieve (similarity search), retrieve-as-tool (AI agent tool)

Rule of thumb: Use LangChain Vector Store for LangChain-native agent/RAG flows. Use the Official Qdrant Node for direct API control, hybrid search, payload operations, and production ingestion pipelines.

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Quick Decision Matrix

| Goal | Use This Node | Mode/Operation | |------|--------------|---------------| | Ingest documents via LangChain chain | LangChain Vector Store | insert | | AI Agent retrieves from Qdrant as tool | LangChain Vector Store | retrieve-as-tool | | Run hybrid (dense+sparse) search | Official Qdrant Node | Search → Query Points | | Create/manage collections | Official Qdrant Node | Collection → Create Collection | | Upsert raw points with custom payloads | Official Qdrant Node | Point → Upsert Points | | Delete points by filter (e.g. file_id) | Official Qdrant Node | Point → Delete Points | | Scroll all points for audit/export | Official Qdrant Node | Point → Scroll Points | | Batch ingest large datasets | Official Qdrant Node | Point → Batch Update Points |

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Canonical Ingestion Pipeline Architecture

[Trigger]
    │
    ▼
[Source Node] ──────────────────────────────────────────────
(Slack, Fireflies, Google Drive, HTTP, DB, etc.)             │
    │                                                         │
    ▼                                                         │
[Split in Batches]  ←── Loop for large datasets              │
    │                                                         │
    ▼                                                         │
[Extract/Normalize]                                          │
(Set node: build content string + raw metadata)              │
    │                                                         │
    ▼                                                         │
[AI: Extract Metadata]                                       │
(Information Extractor or LLM Chain)                         │
Produces: themes, keywords, entities, summary, tags          │
    │                                                         │
    ▼                                                         │
[Text Splitter]                                              │
(Token Splitter or Recursive Character Splitter)             │
chunkSize: 512–2000 tokens, overlap: 10–15%                  │
    │                                                         │
    ▼                                                         │
[Embeddings Node]                                            │
(OpenAI text-embedding-3-large or similar)                   │
    │                                                         │
    ▼                                                         │
[Qdrant Vector Store — insert mode]  OR                      │
[Official Qdrant Node — Upsert Points]                       │
    │                                                         │
    ▼                                                         │
[Wait Node]  ←── Rate limiting / backpressure                │
    │                                                         │
    └─────────────────── back to Split in Batches ───────────┘

See docs/INGESTION-PIPELINE.md for full node-by-node configuration.

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Canonical RAG Retrieval Architecture

[Chat Trigger / Webhook]
    │
    ▼
[AI Agent Node]
    │
    ├── [LLM: Gemini / GPT-4o / Claude]
    ├── [Memory: Window Buffer Memory]
    └── [Tool: Qdrant Vector Store — retrieve-as-tool]
              │
              └── [Embeddings Node]

For hybrid search (dense + sparse), use the Official Qdrant Node → Query Points with a prefetch array combining dense and sparse queries + RRF fusion. See docs/RAG-RETRIEVAL.md.

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Credentials Setup

Official Qdrant Node

Credential type: qdrantApi

Fields: URL (e.g. https://your-cluster.cloud.qdrant.io) + API Key

LangChain Vector Store Node

Credential type: qdrantApi (same credential, shared)

Qdrant Cloud Setup

1. Open https://cloud.qdrant.io → select cluster 2. Copy Endpoint → use as URL 3. Go to API Keys tab → copy key

Local (Docker / AI Starter Kit)

URL: http://qdrant:6333/

Set QDRANT_API_KEY=your_key in docker-compose environment

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Naming Conventions

Use consistent naming across workflows:

| Element | Convention | Example | |---------|-----------|---------| | Collection name | {org}-{source}-{content-type} | acme-slack-messages | | Metadata key for source ID | source_id | "source_id": "C01234-1709123456" | | Metadata key for document ID | doc_id | "doc_id": "file_abc123" | | Metadata key for chunk index | chunk_index | "chunk_index": 3 | | Metadata key for timestamp | created_at | ISO 8601 string | | Metadata key for source type | source_type | "slack", "fireflies", "gdrive" | | Metadata key for channel/folder | source_context | "#engineering" |

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Critical Rules

1. Always set file_id or doc_id in metadata — enables targeted deletion without full collection wipe 2. Always use onError: continueRegularOutput on the Qdrant Vector Store node — prevents single-item failures from crashing the whole batch 3. Always use retryOnFail: true on the Qdrant node for production ingestion 4. Chunk before embedding — never embed full documents; always split first 5. Never store raw text in collection names or keys — normalize to lowercase slug format 6. Use Split in Batches with a Wait node for large datasets — prevents API rate limit errors and memory exhaustion 7. Run metadata extraction BEFORE the text splitter — extract from the full document, then attach metadata to each chunk 8. For delete operations, always add human-in-the-loop confirmation (Telegram sendAndWait, Slack approval, etc.)